Overview

Dataset statistics

Number of variables23
Number of observations142193
Missing cells316559
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.0 MiB
Average record size in memory184.0 B

Variable types

Categorical7
Numeric16

Alerts

Date has a high cardinality: 3436 distinct values High cardinality
MinTemp is highly correlated with MaxTemp and 3 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Rainfall is highly correlated with RainTodayHigh correlation
Evaporation is highly correlated with MinTemp and 4 other fieldsHigh correlation
Sunshine is highly correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Humidity9am is highly correlated with Evaporation and 2 other fieldsHigh correlation
Humidity3pm is highly correlated with Sunshine and 4 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 5 other fieldsHigh correlation
RainToday is highly correlated with RainfallHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 5 other fieldsHigh correlation
Rainfall is highly correlated with RainTodayHigh correlation
Evaporation is highly correlated with MaxTemp and 3 other fieldsHigh correlation
Sunshine is highly correlated with Humidity3pm and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with MaxTemp and 2 other fieldsHigh correlation
Humidity3pm is highly correlated with MaxTemp and 5 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 4 other fieldsHigh correlation
RainToday is highly correlated with RainfallHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Rainfall is highly correlated with RainTodayHigh correlation
Evaporation is highly correlated with MaxTempHigh correlation
Sunshine is highly correlated with Cloud9am and 1 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed3pmHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 1 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 1 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
RainToday is highly correlated with RainfallHigh correlation
Location is highly correlated with MinTemp and 8 other fieldsHigh correlation
MinTemp is highly correlated with Location and 5 other fieldsHigh correlation
MaxTemp is highly correlated with Location and 5 other fieldsHigh correlation
Sunshine is highly correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustDir is highly correlated with Location and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindDir9am is highly correlated with Location and 2 other fieldsHigh correlation
WindDir3pm is highly correlated with Location and 2 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with Location and 6 other fieldsHigh correlation
Humidity3pm is highly correlated with Location and 8 other fieldsHigh correlation
Pressure9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Pressure3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
Cloud9am is highly correlated with Sunshine and 3 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 3 other fieldsHigh correlation
Temp9am is highly correlated with Location and 6 other fieldsHigh correlation
Temp3pm is highly correlated with Location and 5 other fieldsHigh correlation
RainToday is highly correlated with Humidity3pmHigh correlation
RainTomorrow is highly correlated with Sunshine and 2 other fieldsHigh correlation
Evaporation has 60843 (42.8%) missing values Missing
Sunshine has 67816 (47.7%) missing values Missing
WindGustDir has 9330 (6.6%) missing values Missing
WindGustSpeed has 9270 (6.5%) missing values Missing
WindDir9am has 10013 (7.0%) missing values Missing
WindDir3pm has 3778 (2.7%) missing values Missing
WindSpeed3pm has 2630 (1.8%) missing values Missing
Humidity9am has 1774 (1.2%) missing values Missing
Humidity3pm has 3610 (2.5%) missing values Missing
Pressure9am has 14014 (9.9%) missing values Missing
Pressure3pm has 13981 (9.8%) missing values Missing
Cloud9am has 53657 (37.7%) missing values Missing
Cloud3pm has 57094 (40.2%) missing values Missing
Temp3pm has 2726 (1.9%) missing values Missing
Rainfall has 90275 (63.5%) zeros Zeros
Sunshine has 2308 (1.6%) zeros Zeros
WindSpeed9am has 8612 (6.1%) zeros Zeros
Cloud9am has 8587 (6.0%) zeros Zeros
Cloud3pm has 4957 (3.5%) zeros Zeros

Reproduction

Analysis started2021-10-31 12:42:38.594898
Analysis finished2021-10-31 12:43:55.856555
Duration1 minute and 17.26 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2013-12-01
 
49
2014-01-09
 
49
2014-01-11
 
49
2014-01-12
 
49
2014-01-13
 
49
Other values (3431)
141948 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.1%

Sample

1st row2008-12-01
2nd row2008-12-02
3rd row2008-12-03
4th row2008-12-04
5th row2008-12-05

Common Values

ValueCountFrequency (%)
2013-12-0149
 
< 0.1%
2014-01-0949
 
< 0.1%
2014-01-1149
 
< 0.1%
2014-01-1249
 
< 0.1%
2014-01-1349
 
< 0.1%
2014-01-1449
 
< 0.1%
2014-01-1649
 
< 0.1%
2014-01-1949
 
< 0.1%
2014-01-2049
 
< 0.1%
2014-01-2149
 
< 0.1%
Other values (3426)141703
99.7%

Length

2021-10-31T15:43:55.966556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-12-0149
 
< 0.1%
2017-02-1449
 
< 0.1%
2017-05-0349
 
< 0.1%
2017-06-2349
 
< 0.1%
2017-06-2149
 
< 0.1%
2017-06-1549
 
< 0.1%
2017-06-1449
 
< 0.1%
2017-06-1249
 
< 0.1%
2017-06-1049
 
< 0.1%
2017-06-0949
 
< 0.1%
Other values (3426)141703
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3418
Sydney
 
3337
Perth
 
3193
Darwin
 
3192
Hobart
 
3188
Other values (44)
125865 

Length

Max length16
Median length8
Mean length8.703206206
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra3418
 
2.4%
Sydney3337
 
2.3%
Perth3193
 
2.2%
Darwin3192
 
2.2%
Hobart3188
 
2.2%
Brisbane3161
 
2.2%
Adelaide3090
 
2.2%
Bendigo3034
 
2.1%
Townsville3033
 
2.1%
AliceSprings3031
 
2.1%
Other values (39)110516
77.7%

Length

2021-10-31T15:43:56.138056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra3418
 
2.4%
sydney3337
 
2.3%
perth3193
 
2.2%
darwin3192
 
2.2%
hobart3188
 
2.2%
brisbane3161
 
2.2%
adelaide3090
 
2.2%
bendigo3034
 
2.1%
townsville3033
 
2.1%
alicesprings3031
 
2.1%
Other values (39)110516
77.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MinTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct389
Distinct (%)0.3%
Missing637
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean12.18639973
Minimum-8.5
Maximum33.9
Zeros156
Zeros (%)0.1%
Negative3406
Negative (%)2.4%
Memory size1.1 MiB
2021-10-31T15:43:56.670556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.8
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.403282675
Coefficient of variation (CV)0.5254449893
Kurtosis-0.4872527487
Mean12.18639973
Median Absolute Deviation (MAD)4.6
Skewness0.02389982065
Sum1725058
Variance41.00202901
MonotonicityNot monotonic
2021-10-31T15:43:56.881057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11883
 
0.6%
9.6883
 
0.6%
10.2880
 
0.6%
10.5867
 
0.6%
10.8860
 
0.6%
9853
 
0.6%
12850
 
0.6%
10849
 
0.6%
13844
 
0.6%
10.4842
 
0.6%
Other values (379)132945
93.5%
ValueCountFrequency (%)
-8.51
 
< 0.1%
-8.22
 
< 0.1%
-82
 
< 0.1%
-7.81
 
< 0.1%
-7.62
 
< 0.1%
-7.52
 
< 0.1%
-7.31
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-76
< 0.1%
ValueCountFrequency (%)
33.91
 
< 0.1%
31.91
 
< 0.1%
31.81
 
< 0.1%
31.43
< 0.1%
31.21
 
< 0.1%
311
 
< 0.1%
30.72
< 0.1%
30.51
 
< 0.1%
30.31
 
< 0.1%
30.21
 
< 0.1%

MaxTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct505
Distinct (%)0.4%
Missing322
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean23.22678419
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative105
Negative (%)0.1%
Memory size1.1 MiB
2021-10-31T15:43:57.087056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.117618141
Coefficient of variation (CV)0.306440103
Kurtosis-0.2384461504
Mean23.22678419
Median Absolute Deviation (MAD)5.1
Skewness0.2249166146
Sum3295207.1
Variance50.660488
MonotonicityNot monotonic
2021-10-31T15:43:57.303557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20871
 
0.6%
19.8829
 
0.6%
19827
 
0.6%
20.4820
 
0.6%
20.8804
 
0.6%
19.9803
 
0.6%
19.5801
 
0.6%
21799
 
0.6%
18.5793
 
0.6%
18.2792
 
0.6%
Other values (495)133732
94.0%
ValueCountFrequency (%)
-4.81
< 0.1%
-4.11
< 0.1%
-3.81
< 0.1%
-3.71
< 0.1%
-3.21
< 0.1%
-3.12
< 0.1%
-31
< 0.1%
-2.91
< 0.1%
-2.71
< 0.1%
-2.52
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.32
< 0.1%
471
 
< 0.1%
46.91
 
< 0.1%
46.83
< 0.1%
46.72
< 0.1%
46.61
 
< 0.1%
46.51
 
< 0.1%
46.44
< 0.1%
46.32
< 0.1%

Rainfall
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct679
Distinct (%)0.5%
Missing1406
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.349974074
Minimum0
Maximum371
Zeros90275
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:43:57.517556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.465172918
Coefficient of variation (CV)3.602240982
Kurtosis180.0020968
Mean2.349974074
Median Absolute Deviation (MAD)0
Skewness9.888061068
Sum330845.8
Variance71.65915253
MonotonicityNot monotonic
2021-10-31T15:43:57.722055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
090275
63.5%
0.28685
 
6.1%
0.43750
 
2.6%
0.62562
 
1.8%
0.82028
 
1.4%
11747
 
1.2%
1.21515
 
1.1%
1.41365
 
1.0%
1.61187
 
0.8%
1.81088
 
0.8%
Other values (669)26585
 
18.7%
(Missing)1406
 
1.0%
ValueCountFrequency (%)
090275
63.5%
0.1154
 
0.1%
0.28685
 
6.1%
0.364
 
< 0.1%
0.43750
 
2.6%
0.539
 
< 0.1%
0.62562
 
1.8%
0.713
 
< 0.1%
0.82028
 
1.4%
0.915
 
< 0.1%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.61
< 0.1%
216.31
< 0.1%

Evaporation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct356
Distinct (%)0.4%
Missing60843
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean5.469824216
Minimum0
Maximum145
Zeros240
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:43:57.939557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.188536509
Coefficient of variation (CV)0.7657534033
Kurtosis45.06778373
Mean5.469824216
Median Absolute Deviation (MAD)2.4
Skewness3.746833979
Sum444970.2
Variance17.54383809
MonotonicityNot monotonic
2021-10-31T15:43:58.140056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43282
 
2.3%
82574
 
1.8%
2.22057
 
1.4%
21996
 
1.4%
2.61975
 
1.4%
2.41963
 
1.4%
1.81945
 
1.4%
31937
 
1.4%
3.41934
 
1.4%
3.21918
 
1.3%
Other values (346)59769
42.0%
(Missing)60843
42.8%
ValueCountFrequency (%)
0240
 
0.2%
0.18
 
< 0.1%
0.2497
 
0.3%
0.310
 
< 0.1%
0.4760
0.5%
0.514
 
< 0.1%
0.61082
0.8%
0.724
 
< 0.1%
0.81358
1.0%
0.928
 
< 0.1%
ValueCountFrequency (%)
1451
< 0.1%
86.21
< 0.1%
82.41
< 0.1%
81.21
< 0.1%
77.31
< 0.1%
74.81
< 0.1%
72.21
< 0.1%
70.41
< 0.1%
701
< 0.1%
68.82
< 0.1%

Sunshine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing67816
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean7.624853113
Minimum0
Maximum14.5
Zeros2308
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:43:58.367555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.9
median8.5
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.781524994
Coefficient of variation (CV)0.495947258
Kurtosis-0.8203636955
Mean7.624853113
Median Absolute Deviation (MAD)2.6
Skewness-0.5029112767
Sum567113.7
Variance14.29993128
MonotonicityNot monotonic
2021-10-31T15:43:58.584555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02308
 
1.6%
10.71087
 
0.8%
111078
 
0.8%
10.81058
 
0.7%
10.51018
 
0.7%
10.91013
 
0.7%
10.3999
 
0.7%
10.2985
 
0.7%
10973
 
0.7%
11.1967
 
0.7%
Other values (135)62891
44.2%
(Missing)67816
47.7%
ValueCountFrequency (%)
02308
1.6%
0.1533
 
0.4%
0.2511
 
0.4%
0.3422
 
0.3%
0.4319
 
0.2%
0.5315
 
0.2%
0.6293
 
0.2%
0.7338
 
0.2%
0.8314
 
0.2%
0.9318
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.34
 
< 0.1%
14.22
 
< 0.1%
14.16
 
< 0.1%
1415
 
< 0.1%
13.922
 
< 0.1%
13.856
 
< 0.1%
13.7117
0.1%
13.6180
0.1%
13.5181
0.1%

WindGustDir
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing9330
Missing (%)6.6%
Memory size1.1 MiB
W
9780 
SE
9309 
E
9071 
N
9033 
SSE
8993 
Other values (11)
86677 

Length

Max length3
Median length2
Mean length2.195901041
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW

Common Values

ValueCountFrequency (%)
W9780
 
6.9%
SE9309
 
6.5%
E9071
 
6.4%
N9033
 
6.4%
SSE8993
 
6.3%
S8949
 
6.3%
WSW8901
 
6.3%
SW8797
 
6.2%
SSW8610
 
6.1%
WNW8066
 
5.7%
Other values (6)43354
30.5%
(Missing)9330
 
6.6%

Length

2021-10-31T15:43:58.797558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w9780
 
7.4%
se9309
 
7.0%
e9071
 
6.8%
n9033
 
6.8%
sse8993
 
6.8%
s8949
 
6.7%
wsw8901
 
6.7%
sw8797
 
6.6%
ssw8610
 
6.5%
wnw8066
 
6.1%
Other values (6)43354
32.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindGustSpeed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)0.1%
Missing9270
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean39.98429166
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:43:58.984056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.58880077
Coefficient of variation (CV)0.3398534825
Kurtosis1.417854632
Mean39.98429166
Median Absolute Deviation (MAD)9
Skewness0.8743045673
Sum5314832
Variance184.6555062
MonotonicityNot monotonic
2021-10-31T15:43:59.213058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359070
 
6.4%
398656
 
6.1%
318310
 
5.8%
377903
 
5.6%
337814
 
5.5%
417236
 
5.1%
306943
 
4.9%
436513
 
4.6%
286382
 
4.5%
445341
 
3.8%
Other values (57)58755
41.3%
(Missing)9270
 
6.5%
ValueCountFrequency (%)
61
 
< 0.1%
718
 
< 0.1%
991
 
0.1%
11190
 
0.1%
13529
 
0.4%
15829
 
0.6%
171375
1.0%
191728
1.2%
202598
1.8%
222787
2.0%
ValueCountFrequency (%)
1353
 
< 0.1%
1301
 
< 0.1%
1262
 
< 0.1%
1242
 
< 0.1%
1222
 
< 0.1%
1203
 
< 0.1%
1174
< 0.1%
1155
< 0.1%
1138
< 0.1%
1113
 
< 0.1%

WindDir9am
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing10013
Missing (%)7.0%
Memory size1.1 MiB
N
11393 
SE
9162 
E
9024 
SSE
8966 
NW
8552 
Other values (11)
85083 

Length

Max length3
Median length2
Mean length2.184309275
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE

Common Values

ValueCountFrequency (%)
N11393
 
8.0%
SE9162
 
6.4%
E9024
 
6.3%
SSE8966
 
6.3%
NW8552
 
6.0%
S8493
 
6.0%
W8260
 
5.8%
SW8237
 
5.8%
NNE7948
 
5.6%
NNW7840
 
5.5%
Other values (6)44305
31.2%
(Missing)10013
 
7.0%

Length

2021-10-31T15:43:59.429056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n11393
 
8.6%
se9162
 
6.9%
e9024
 
6.8%
sse8966
 
6.8%
nw8552
 
6.5%
s8493
 
6.4%
w8260
 
6.2%
sw8237
 
6.2%
nne7948
 
6.0%
nnw7840
 
5.9%
Other values (6)44305
33.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindDir3pm
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing3778
Missing (%)2.7%
Memory size1.1 MiB
SE
10663 
W
9911 
S
9598 
WSW
9329 
SW
9182 
Other values (11)
89732 

Length

Max length3
Median length2
Mean length2.208806849
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW

Common Values

ValueCountFrequency (%)
SE10663
 
7.5%
W9911
 
7.0%
S9598
 
6.7%
WSW9329
 
6.6%
SW9182
 
6.5%
SSE9142
 
6.4%
N8667
 
6.1%
WNW8656
 
6.1%
NW8468
 
6.0%
ESE8382
 
5.9%
Other values (6)46417
32.6%

Length

2021-10-31T15:43:59.614057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se10663
 
7.7%
w9911
 
7.2%
s9598
 
6.9%
wsw9329
 
6.7%
sw9182
 
6.6%
sse9142
 
6.6%
n8667
 
6.3%
wnw8656
 
6.3%
nw8468
 
6.1%
ese8382
 
6.1%
Other values (6)46417
33.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindSpeed9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)< 0.1%
Missing1348
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean14.001988
Minimum0
Maximum130
Zeros8612
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:43:59.821056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.893337098
Coefficient of variation (CV)0.6351481731
Kurtosis1.226555132
Mean14.001988
Median Absolute Deviation (MAD)6
Skewness0.77549369
Sum1972110
Variance79.09144474
MonotonicityNot monotonic
2021-10-31T15:44:00.049557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
913400
 
9.4%
1312851
 
9.0%
1111514
 
8.1%
1710599
 
7.5%
710587
 
7.4%
1510396
 
7.3%
68989
 
6.3%
08612
 
6.1%
198579
 
6.0%
207904
 
5.6%
Other values (33)37414
26.3%
ValueCountFrequency (%)
08612
6.1%
24544
 
3.2%
46292
4.4%
68989
6.3%
710587
7.4%
913400
9.4%
1111514
8.1%
1312851
9.0%
1510396
7.3%
1710599
7.5%
ValueCountFrequency (%)
1301
 
< 0.1%
872
 
< 0.1%
831
 
< 0.1%
744
 
< 0.1%
721
 
< 0.1%
692
 
< 0.1%
673
 
< 0.1%
658
< 0.1%
638
< 0.1%
6111
< 0.1%

WindSpeed3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)< 0.1%
Missing2630
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean18.63757586
Minimum0
Maximum87
Zeros1096
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:00.254055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile34.8
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.803345036
Coefficient of variation (CV)0.4723438875
Kurtosis0.775864544
Mean18.63757586
Median Absolute Deviation (MAD)6
Skewness0.6314326033
Sum2601116
Variance77.49888383
MonotonicityNot monotonic
2021-10-31T15:44:00.444058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1312338
 
8.7%
1712306
 
8.7%
2011504
 
8.1%
1511301
 
7.9%
1911034
 
7.8%
119844
 
6.9%
99577
 
6.7%
248846
 
6.2%
228410
 
5.9%
286395
 
4.5%
Other values (34)38008
26.7%
ValueCountFrequency (%)
01096
 
0.8%
21012
 
0.7%
42213
 
1.6%
63744
 
2.6%
75813
4.1%
99577
6.7%
119844
6.9%
1312338
8.7%
1511301
7.9%
1712306
8.7%
ValueCountFrequency (%)
871
 
< 0.1%
832
 
< 0.1%
781
 
< 0.1%
762
 
< 0.1%
741
 
< 0.1%
722
 
< 0.1%
693
 
< 0.1%
671
 
< 0.1%
6517
< 0.1%
6313
< 0.1%

Humidity9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing1774
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean68.84381031
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:00.649557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.05129254
Coefficient of variation (CV)0.2767321049
Kurtosis-0.03924572083
Mean68.84381031
Median Absolute Deviation (MAD)13
Skewness-0.4828207735
Sum9666979
Variance362.9517473
MonotonicityNot monotonic
2021-10-31T15:44:00.864560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993350
 
2.4%
702985
 
2.1%
692962
 
2.1%
682961
 
2.1%
652952
 
2.1%
712939
 
2.1%
662916
 
2.1%
672895
 
2.0%
642867
 
2.0%
752859
 
2.0%
Other values (91)110733
77.9%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
< 0.1%
28
 
< 0.1%
310
 
< 0.1%
420
 
< 0.1%
527
 
< 0.1%
637
< 0.1%
743
< 0.1%
856
< 0.1%
971
< 0.1%
ValueCountFrequency (%)
1002827
2.0%
993350
2.4%
982063
1.5%
971757
1.2%
961577
1.1%
951589
1.1%
941730
1.2%
931833
1.3%
921728
1.2%
911834
1.3%

Humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing3610
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.48260609
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:01.080058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.79777184
Coefficient of variation (CV)0.4039766714
Kurtosis-0.511101194
Mean51.48260609
Median Absolute Deviation (MAD)14
Skewness0.03451544293
Sum7134614
Variance432.5473137
MonotonicityNot monotonic
2021-10-31T15:44:01.290057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522699
 
1.9%
552685
 
1.9%
572679
 
1.9%
532650
 
1.9%
592639
 
1.9%
582604
 
1.8%
542595
 
1.8%
512577
 
1.8%
562574
 
1.8%
502573
 
1.8%
Other values (91)112308
79.0%
(Missing)3610
 
2.5%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
363
 
< 0.1%
4113
 
0.1%
5156
 
0.1%
6239
0.2%
7302
0.2%
8420
0.3%
9478
0.3%
ValueCountFrequency (%)
100393
0.3%
99428
0.3%
98588
0.4%
97393
0.3%
96451
0.3%
95452
0.3%
94550
0.4%
93593
0.4%
92636
0.4%
91601
0.4%

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing14014
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1017.653758
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:01.497056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.105475712
Coefficient of variation (CV)0.006982213403
Kurtosis0.2361999825
Mean1017.653758
Median Absolute Deviation (MAD)4.7
Skewness-0.09621089388
Sum130441841.1
Variance50.48778509
MonotonicityNot monotonic
2021-10-31T15:44:01.706055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4804
 
0.6%
1017.9779
 
0.5%
1018.7764
 
0.5%
1018761
 
0.5%
1015.9757
 
0.5%
1017.3756
 
0.5%
1017.8755
 
0.5%
1016.3753
 
0.5%
1017.2745
 
0.5%
1015.5744
 
0.5%
Other values (536)120561
84.8%
(Missing)14014
 
9.9%
ValueCountFrequency (%)
980.51
< 0.1%
9821
< 0.1%
982.21
< 0.1%
982.31
< 0.1%
982.92
< 0.1%
983.71
< 0.1%
983.91
< 0.1%
984.41
< 0.1%
984.62
< 0.1%
9851
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%
1040.33
< 0.1%
1040.22
< 0.1%
1040.13
< 0.1%
10401
 
< 0.1%
1039.93
< 0.1%

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing13981
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean1015.258204
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:01.926056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.036676783
Coefficient of variation (CV)0.006930923344
Kurtosis0.1325207865
Mean1015.258204
Median Absolute Deviation (MAD)4.8
Skewness-0.04619761861
Sum130168284.8
Variance49.51482016
MonotonicityNot monotonic
2021-10-31T15:44:02.132056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.5773
 
0.5%
1015.3767
 
0.5%
1015.7763
 
0.5%
1015.6761
 
0.5%
1015.1752
 
0.5%
1015.8751
 
0.5%
1013.5751
 
0.5%
1015.4745
 
0.5%
1016738
 
0.5%
1014.8735
 
0.5%
Other values (539)120676
84.9%
(Missing)13981
 
9.8%
ValueCountFrequency (%)
977.11
< 0.1%
978.21
< 0.1%
9791
< 0.1%
980.22
< 0.1%
981.21
< 0.1%
981.41
< 0.1%
981.91
< 0.1%
982.21
< 0.1%
982.61
< 0.1%
982.91
< 0.1%
ValueCountFrequency (%)
1039.61
 
< 0.1%
1038.91
 
< 0.1%
1038.51
 
< 0.1%
1038.41
 
< 0.1%
1038.21
 
< 0.1%
10381
 
< 0.1%
1037.92
< 0.1%
1037.82
< 0.1%
1037.73
< 0.1%
1037.61
 
< 0.1%

Cloud9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing53657
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean4.437189392
Minimum0
Maximum9
Zeros8587
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:02.317059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.887015526
Coefficient of variation (CV)0.6506405904
Kurtosis-1.5411594
Mean4.437189392
Median Absolute Deviation (MAD)3
Skewness-0.2242855389
Sum392851
Variance8.334858646
MonotonicityNot monotonic
2021-10-31T15:44:02.457556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
719749
 
13.9%
115558
 
10.9%
814389
 
10.1%
08587
 
6.0%
68072
 
5.7%
26442
 
4.5%
35854
 
4.1%
55510
 
3.9%
44373
 
3.1%
92
 
< 0.1%
(Missing)53657
37.7%
ValueCountFrequency (%)
08587
6.0%
115558
10.9%
26442
 
4.5%
35854
 
4.1%
44373
 
3.1%
55510
 
3.9%
68072
5.7%
719749
13.9%
814389
10.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
814389
10.1%
719749
13.9%
68072
5.7%
55510
 
3.9%
44373
 
3.1%
35854
 
4.1%
26442
 
4.5%
115558
10.9%
08587
6.0%

Cloud3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing57094
Missing (%)40.2%
Infinite0
Infinite (%)0.0%
Mean4.5031669
Minimum0
Maximum9
Zeros4957
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-10-31T15:44:02.609059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.72063253
Coefficient of variation (CV)0.604159826
Kurtosis-1.457932583
Mean4.5031669
Median Absolute Deviation (MAD)2
Skewness-0.2240923649
Sum383215
Variance7.401841365
MonotonicityNot monotonic
2021-10-31T15:44:02.754056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
718052
 
12.7%
114827
 
10.4%
812407
 
8.7%
68869
 
6.2%
27153
 
5.0%
36836
 
4.8%
56743
 
4.7%
45254
 
3.7%
04957
 
3.5%
91
 
< 0.1%
(Missing)57094
40.2%
ValueCountFrequency (%)
04957
 
3.5%
114827
10.4%
27153
 
5.0%
36836
 
4.8%
45254
 
3.7%
56743
 
4.7%
68869
6.2%
718052
12.7%
812407
8.7%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
812407
8.7%
718052
12.7%
68869
6.2%
56743
 
4.7%
45254
 
3.7%
36836
 
4.8%
27153
 
5.0%
114827
10.4%
04957
 
3.5%

Temp9am
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct440
Distinct (%)0.3%
Missing904
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.98750858
Minimum-7.2
Maximum40.2
Zeros35
Zeros (%)< 0.1%
Negative420
Negative (%)0.3%
Memory size1.1 MiB
2021-10-31T15:44:02.929057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.492838325
Coefficient of variation (CV)0.3822125119
Kurtosis-0.3491547666
Mean16.98750858
Median Absolute Deviation (MAD)4.6
Skewness0.09138682047
Sum2400148.1
Variance42.15694952
MonotonicityNot monotonic
2021-10-31T15:44:03.468055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17901
 
0.6%
13.8887
 
0.6%
14.8873
 
0.6%
16869
 
0.6%
16.6855
 
0.6%
14855
 
0.6%
15852
 
0.6%
16.5844
 
0.6%
13831
 
0.6%
15.4827
 
0.6%
Other values (430)132695
93.3%
(Missing)904
 
0.6%
ValueCountFrequency (%)
-7.21
 
< 0.1%
-71
 
< 0.1%
-6.21
 
< 0.1%
-5.91
 
< 0.1%
-5.62
 
< 0.1%
-5.52
 
< 0.1%
-5.32
 
< 0.1%
-5.25
< 0.1%
-4.81
 
< 0.1%
-4.52
 
< 0.1%
ValueCountFrequency (%)
40.21
< 0.1%
39.41
< 0.1%
39.11
< 0.1%
391
< 0.1%
38.91
< 0.1%
38.61
< 0.1%
38.31
< 0.1%
38.21
< 0.1%
381
< 0.1%
37.91
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct500
Distinct (%)0.4%
Missing2726
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean21.68723497
Minimum-5.4
Maximum46.7
Zeros16
Zeros (%)< 0.1%
Negative171
Negative (%)0.1%
Memory size1.1 MiB
2021-10-31T15:44:03.675555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.937593869
Coefficient of variation (CV)0.3198929636
Kurtosis-0.1464607024
Mean21.68723497
Median Absolute Deviation (MAD)4.9
Skewness0.2400541927
Sum3024653.6
Variance48.13020868
MonotonicityNot monotonic
2021-10-31T15:44:03.879056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20871
 
0.6%
19858
 
0.6%
18.4856
 
0.6%
18.5856
 
0.6%
17.8845
 
0.6%
19.2826
 
0.6%
19.4825
 
0.6%
18821
 
0.6%
19.3821
 
0.6%
17814
 
0.6%
Other values (490)131074
92.2%
(Missing)2726
 
1.9%
ValueCountFrequency (%)
-5.41
 
< 0.1%
-5.11
 
< 0.1%
-4.41
 
< 0.1%
-4.21
 
< 0.1%
-4.11
 
< 0.1%
-41
 
< 0.1%
-3.92
< 0.1%
-3.81
 
< 0.1%
-3.73
< 0.1%
-3.53
< 0.1%
ValueCountFrequency (%)
46.71
 
< 0.1%
46.21
 
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.82
< 0.1%
45.41
 
< 0.1%
45.32
< 0.1%
45.22
< 0.1%
451
 
< 0.1%
44.91
 
< 0.1%

RainToday
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1406
Missing (%)1.0%
Memory size1.1 MiB
0.0
109332 
1.0
31455 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0109332
76.9%
1.031455
 
22.1%
(Missing)1406
 
1.0%

Length

2021-10-31T15:44:04.057559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-31T15:44:04.162055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0109332
77.7%
1.031455
 
22.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RainTomorrow
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
110316 
1
31877 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110316
77.6%
131877
 
22.4%

Length

2021-10-31T15:44:04.270056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-31T15:44:04.373055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0110316
77.6%
131877
 
22.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-31T15:43:49.153556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:55.602556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:59.522058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:03.027056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:06.491556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:10.248055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:13.659557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:17.279559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:20.753056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:24.616555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:28.226056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:31.685056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:35.559556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:39.009556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:42.147564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:45.625057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:49.362557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:55.818053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:59.749059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:03.243056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:06.690559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:10.471055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:13.894054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:17.497559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:20.988056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:24.836558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:28.443557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:31.895058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:35.774556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:39.219556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:42.347056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:45.837059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:49.574556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:56.043056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:59.984056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:03.464057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:06.886056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:10.663059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:14.119056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:17.721556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:21.556556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:25.083555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:28.672057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:32.146056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:35.987056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:39.405555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:42.530057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:46.076555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:49.777558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:56.256056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:00.204556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:03.673555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:07.084558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:10.873556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:14.342063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:17.933557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:21.764056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:25.302557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:28.876560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:32.363557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:36.200556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:39.603055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:42.730557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:46.279057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:49.976056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:56.463058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:00.409056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:03.875057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:07.306055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:11.115057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:14.549554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:18.129557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:21.979056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:25.531057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:29.079057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:32.575558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:36.418056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:39.812055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:42.927055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:46.499059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:50.197056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:56.692556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:00.637555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:04.104556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:07.534056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:11.345054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:14.783056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:18.366557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:22.243057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:25.775056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:29.313558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:32.800056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:36.651559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:40.036056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:43.129560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:46.743056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:50.423558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:56.928055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:00.878555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:04.342560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:07.732059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:11.564557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:15.024555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:18.599555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:22.476559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:26.021058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:29.549554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:33.377056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:36.881557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:40.233559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:43.340556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:46.973056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:50.642556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:57.162058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:01.108056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:04.571556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:07.948057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:11.778559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:15.258556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:18.835058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:22.708058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:26.266567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:29.769556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:33.609555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:37.090059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:40.429557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:43.524555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:47.200056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:50.854556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:57.377555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:01.322559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:04.788556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:08.166057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:11.998558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:15.496056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:19.047057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:22.923556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:26.502059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:29.986554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:33.817557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:37.304554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:40.625559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:43.709556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:47.462061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:51.073056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:57.596558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:01.548059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:05.012056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:08.380059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:12.216555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:15.728558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:19.271557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:23.140556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:26.727057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:30.208055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:34.032057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:37.522557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:40.821555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:43.902556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:47.700059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:51.275555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:57.835559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:01.775558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:05.227556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:08.601057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:12.416055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:15.961559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:19.483053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:23.351557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:26.943556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:30.417556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:34.251556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:37.740556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.009556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:44.442058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:47.908556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:51.490055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:58.090557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:02.000059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:05.451558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:08.822558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:12.624056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:16.196555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:19.712558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:23.570056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:27.167056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:30.657555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:34.484056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:37.967559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.209056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:44.646555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:48.127058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:51.691556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:58.299556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:02.204056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:05.664557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:09.042055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:12.851556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:16.415558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:19.913555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:23.775555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:27.376055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:30.863056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:34.720556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:38.181558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.411559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:44.849559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:48.335055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:51.881556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:58.505055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:02.391555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:05.859560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:09.241059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:13.041557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:16.612059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:20.109058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:23.962058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:27.572055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:31.052055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:34.922555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:38.376058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.593556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:45.036056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:48.520056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:52.090555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:59.068555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:02.603556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:06.080057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:09.453055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:13.233556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:16.830557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:20.321057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:24.174557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:27.793555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:31.264057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:35.133556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:38.589555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.780557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:45.222057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:48.732056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:52.298055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:42:59.304054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:02.818056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:06.295055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:09.680059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:13.426556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:17.052556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:20.541555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:24.400056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:28.012556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:31.477555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:35.347057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:38.807057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:41.964059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:45.412555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-31T15:43:48.943556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-31T15:44:04.499056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-31T15:44:04.866056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-31T15:44:05.234056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-31T15:44:05.560058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-31T15:44:05.816056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-31T15:43:52.689056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-31T15:43:53.621058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-31T15:43:54.746556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-31T15:43:55.491556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0WWNW20.024.071.022.01007.71007.18.0NaN16.921.80.00
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNWWSW4.022.044.025.01010.61007.8NaNNaN17.224.30.00
22008-12-03Albury12.925.70.0NaNNaNWSW46.0WWSW19.026.038.030.01007.61008.7NaN2.021.023.20.00
32008-12-04Albury9.228.00.0NaNNaNNE24.0SEE11.09.045.016.01017.61012.8NaNNaN18.126.50.00
42008-12-05Albury17.532.31.0NaNNaNW41.0ENENW7.020.082.033.01010.81006.07.08.017.829.70.00
52008-12-06Albury14.629.70.2NaNNaNWNW56.0WW19.024.055.023.01009.21005.4NaNNaN20.628.90.00
62008-12-07Albury14.325.00.0NaNNaNW50.0SWW20.024.049.019.01009.61008.21.0NaN18.124.60.00
72008-12-08Albury7.726.70.0NaNNaNW35.0SSEW6.017.048.019.01013.41010.1NaNNaN16.325.50.00
82008-12-09Albury9.731.90.0NaNNaNNNW80.0SENW7.028.042.09.01008.91003.6NaNNaN18.330.20.01
92008-12-10Albury13.130.11.4NaNNaNW28.0SSSE15.011.058.027.01007.01005.7NaNNaN20.128.21.00

Last rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1421832017-06-15Uluru2.622.50.0NaNNaNS19.0SE9.07.059.024.01025.01021.4NaNNaN8.822.10.00
1421842017-06-16Uluru5.224.30.0NaNNaNE24.0SEE11.011.053.024.01023.81020.0NaNNaN12.323.30.00
1421852017-06-17Uluru6.423.40.0NaNNaNESE31.0SESE15.017.053.025.01025.81023.0NaNNaN11.223.10.00
1421862017-06-18Uluru8.020.70.0NaNNaNESE41.0SEE19.026.056.032.01028.11024.3NaN7.011.620.00.00
1421872017-06-19Uluru7.420.60.0NaNNaNE35.0ESEE15.017.063.033.01027.21023.3NaNNaN11.020.30.00
1421882017-06-20Uluru3.521.80.0NaNNaNE31.0ESEE15.013.059.027.01024.71021.2NaNNaN9.420.90.00
1421892017-06-21Uluru2.823.40.0NaNNaNE31.0SEENE13.011.051.024.01024.61020.3NaNNaN10.122.40.00
1421902017-06-22Uluru3.625.30.0NaNNaNNNW22.0SEN13.09.056.021.01023.51019.1NaNNaN10.924.50.00
1421912017-06-23Uluru5.426.90.0NaNNaNN37.0SEWNW9.09.053.024.01021.01016.8NaNNaN12.526.10.00
1421922017-06-24Uluru7.827.00.0NaNNaNSE28.0SSEN13.07.051.024.01019.41016.53.02.015.126.00.00